Multivariate Imputation by Chained Equations (MICE) is a method for imputing missing values for multivariate data [1]}, [2]}.
The multivariate in MICE highlights its use of multiple variables to impute missing values.
The MICE algorithm works under the assumption that the data are missing at random (MAR).
MAR was first detailed in the paper [3]}.
It implies that errors in the system or with users cause the missing entries and not intrinsic features of the object being measured.
Furthermore, MAR implies the possibility that the missing entries can be inferred by the other variables present in the data [4]}.
Indeed, this is a strong assumption, and it is our first step to deal with missing data. However, we know that selection biases play an important role for the flux detection.
Although this problem is mitigated for the gamma-ray sources, for the G-band magnitude, one can argue that, e.g., BL Lacs are systematically fainter than FSRQs and below the Gaia limiting magnitude.
A more in-depth analysis to take this problem into account is worthwhile, but this is beyond the scope of the current paper.
